Schoolchildren’ depression and anxiety risk factors assessment and prediction: Machine learning techniques performance analysis

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dc.authoridRadwan Qasrawi / 0000-0001-8671-7026en_US
dc.authorscopusidRadwan Qasrawi / 57212263325
dc.authorwosidRadwan Qasrawi / AAA-6245-2019en_US
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorPolo, Stephanny Vicuna
dc.contributor.authorAl-Halawa, Diala Abu
dc.contributor.authorHallaq, Sameh
dc.contributor.authorAbdeen, Ziad
dc.date.accessioned2022-07-17T07:56:09Z
dc.date.available2022-07-17T07:56:09Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractBackground: Depression and anxiety symptoms in early childhood have a major effect on children's mental health growth and cognitive development. Studying the effect of mental health problems on cognitive development has gained researchers' attention for the last two decades. Objective: In this paper, we seek to use machine learning techniques to predict the risk factors associated with school children's depression and anxiety. Methods: The study data consisted of 5685 students in grades 5-9, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors school children questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. Five machine learning techniques (Random Forest, Neural Network, Decision Tree, Support Vector Machine, and Naïve Bayes) were used for prediction. Results: The results indicated that the SVM and Random Forest model had the highest accuracy levels (SVM= 92.5%, RF=76.4%; SVM=92.4%, RF=78.6%) for depression and anxiety respectively. Thus, the SVM and Random Forest had the best performance in classifying and predicting the student's depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting depression and anxiety scales. Conclusions: Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The ML techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.en_US
dc.identifier.citationQasrawi R, Vicuna Polo S, Abu Al-Halawah D, Hallaq S, Abdeen Z Schoolchildren’ Depression and Anxiety Risk Factors Assessment and Prediction: Machine Learning Techniques Performance Analysis JMIR Formative Research. 29/04/2022:32736 (forthcoming/in press)en_US
dc.identifier.doi10.2196/preprints.32736en_US
dc.identifier.issn2369-1999en_US
dc.identifier.pmid35665695en_US
dc.identifier.urihttp://doi.org/10.2196/preprints.32736
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3001
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorQasrawi, Radwan
dc.language.isoenen_US
dc.publisherJMIRen_US
dc.relation.ispartofJMIR Formative Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine Learningen_US
dc.subjectDepressionen_US
dc.subjectAnxietyen_US
dc.subjectSchoolchildrenen_US
dc.subjectPredictionen_US
dc.subjectRandom Foresten_US
dc.titleSchoolchildren’ depression and anxiety risk factors assessment and prediction: Machine learning techniques performance analysisen_US
dc.typeArticleen_US

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